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Gpu Performance Engineer Jobs in Toronto, ON (NOW HIRING)

Quincus

Machine Learning Engineer

Toronto, ON

... performance of deep learning and reinforcement learning models using techniques such as ... GPU computing to accelerate model training and inference. - Develop and deploy production workflows ...

Serve Robotics

Lead Machine Learning Engineer

Toronto, ON · Remote

$225K - $260K/yr

... performance autonomy models. By optimizing distributed training pipelines, neural network ... This includes ensuring data is efficiently loaded, distributed, and processed across large GPU ...

Hitachi

Forward Deployed Engineer

Toronto, ON · On-site +1

The FDE role blends hands-on engineering, limited-scopeeconomic modeling, and customer-facing ... Perform workload and performance evaluations forcompute, networking, storage, and GPU-accelerated ...

YO AI Labs

Game Developer - Remote

Toronto, ON · Remote

Our customer's team is seeking a Game Tools & Modding Engineer with strong rendering and engine ... Optimize performance across CPU/GPU, memory, and rendering workloads. * Build and extend tools ...

Amd

SoC Data Path Engineer

Thornhill, ON · Hybrid

... GPU, accelerator, and chiplet subsystems. You will play a critical role in ensuring performance ... As a SoC Data Path Engineer, you will drive balanced, scalable, and automated solutions. In this ...

Nvidia

Solutions Architect, Networking

Toronto, ON · On-site

You will also collaborate with a diverse set of internal engineering, product, and business teams on performance analysis and modeling of these large GPU clusters. You should be comfortable working ...

Deloitte

AI Research Engineer

Toronto, ON · On-site

Generate regular reports on model accuracy, drift, and performance. Debug, optimize, and enhance ... Run and optimize multi-GPU training and inference, leveraging vLLM for high-throughput, low-latency ...

Amd

Sr. Manager Packaging Engineering

Thornhill, ON · Hybrid

Lead end-to-end execution of multiple Data Center GPU / MI package design programs, ensuring ... Conduct performance reviews, talent development planning, and succession management. Technical ...

Amd

Senior Staff Software Development Eng

Thornhill, ON · Hybrid

Most importantly, you are constantly thinking of ways to improve performance of multi-node GPU ... Familiarity with GPU programming in HIP or CUDA * In-depth knowledge of best practices in software ...

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Gpu Performance Engineer information

What are some common challenges faced by a GPU performance engineer when optimizing graphics workloads?

GPU Performance Engineers often encounter challenges such as identifying performance bottlenecks within complex graphics pipelines, balancing resource utilization, and achieving optimal frame rates across diverse hardware configurations. They must use specialized profiling tools and collaborate closely with developers, driver engineers, and QA teams to address issues like memory bandwidth limitations or shader inefficiencies. Staying updated with rapidly evolving GPU architectures and optimizing for both current and next-generation hardware are also key aspects of the role.

What is a GPU performance engineer?

A GPU Performance Engineer is a specialist who analyzes, optimizes, and improves the performance of graphics processing units (GPUs). They work on identifying bottlenecks, optimizing code, and ensuring that GPU hardware and software deliver maximum efficiency and speed. Their role may involve working with drivers, firmware, and applications to enhance graphics and compute workloads. This job is essential in industries like gaming, AI, and high-performance computing where GPU efficiency directly impacts user experience and system performance.

What are the key skills and qualifications needed to thrive as a GPU performance engineer, and why are they important?

To thrive as a GPU Performance Engineer, you need a strong background in computer architecture, programming (C/C++), and a degree in computer science, electrical engineering, or a related field. Proficiency with GPU profiling tools (e.g., NVIDIA Nsight, AMD Radeon GPU Profiler), performance analysis frameworks, and parallel computing libraries like CUDA or OpenCL is typically required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with developers and debugging performance bottlenecks. These skills and qualities are essential for optimizing GPU performance, ensuring efficient software-hardware interaction, and delivering high-quality graphics or compute solutions.

What is the difference between Gpu Performance Engineer vs Gpu Hardware Engineer?

AspectGpu Performance EngineerGpu Hardware Engineer
Primary FocusOptimizing GPU performance, benchmarking, and tuning softwareDesigning, developing, and testing GPU hardware components
Required SkillsProgramming, performance analysis, GPU architecture knowledgeHardware design, circuit analysis, FPGA/ASIC experience
Work EnvironmentSoftware development teams, labs for testing performanceHardware labs, manufacturing facilities, R&D centers
Common CertificationsNone specific, often requires computer engineering or related degreesElectrical engineering, VLSI design certifications

The Gpu Performance Engineer primarily focuses on optimizing and testing GPU software performance, while the Gpu Hardware Engineer designs and develops the physical GPU components. Both roles require a strong background in computer engineering, but differ in their core responsibilities and work environments.

What job categories do people searching Gpu Performance Engineer jobs in Toronto, ON look for? The top searched job categories for Gpu Performance Engineer jobs in Toronto, ON are:
Gpu Performance Engineer jobs near you
Infographic showing various Gpu Performance Engineer job openings in Toronto, ON as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

Machine Learning Engineer

Quincus

Toronto, ON

Full-time

Re-posted 7 days ago

Job description

"Make every logistics journey your best one yet"

The Company.
Founded in 2014, Quincus is a B2B supply chain operating SaaS platform headquartered in Singapore. We solve today's global supply chain challenges with groundbreaking technology. Using AI and machine learning, we have digitized and optimized the logistics process while giving customers full transparency into their supply chain. 
 
Quincus was founded by two visionary entrepreneurs who possess more than a decade of experience in tech. Chief Product Officer Katherina-Olivia Lacey is leading a tech revolution in this space while empowering women in the supply chain industry. Jonathan E. Savoir, Chief Executive Officer, appeared on Forbes' 30 Under 30 Asia List in 2020, and also serves on the boards of several startups.  

Overview.
Quincus Research is building the next generation of intelligent systems for all Quincus products. To achieve this, we're working on projects that utilize the latest computer science techniques developed by skilled software engineers and research scientists. Quincus Research teams collaborate closely with other teams across Quincus, maintaining the flexibility and versatility required to adapt new projects and focuses that meet the demands of the world's fast-paced business needs. 

Job Overview. 
We are looking for a highly motivated and experienced machine learning engineer to join our team and help us develop and deploy deep learning and reinforcement learning algorithms at scale. As a machine learning engineer, you will be responsible for designing and implementing scalable systems for serving models, optimizing inference performance, and managing production workflows. 

Responsibilities: 
- Design and implement scalable systems for serving deep learning and reinforcement learning models.
- Optimize inference performance of deep learning and reinforcement learning models using techniques such as quantization, pruning, and distillation.
- Utilize GPU computing to accelerate model training and inference.
- Develop and deploy production workflows for training and serving machine learning models.
- Collaborate with data scientists and software engineers to design and implement machine learning systems.
- Monitor and improve the performance of machine learning models in production.
- Stay up-to-date with the latest research and techniques in deep learning and reinforcement learning. 
 
Qualifications:
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
- 3+ years of experience in software engineering or machine learning engineering.
- Strong programming skills in Python (C++ or Java a plus)
- Experience with deep learning frameworks such as TensorFlow or PyTorch.
- Experience with GPU programming using CUDA, OpenCL, or similar libraries.
- Experience with distributed systems and cloud computing platforms such as Kubernetes, Docker, GCP, and AWS. 

Preferred Qualifications: 
- Ph.D. in Computer Science, Electrical Engineering, or a related field.
- 5+ years of experience in software engineering or machine learning engineering.
- Experience with reinforcement learning algorithms and frameworks.
- Experience with production deployment of machine learning models and implementation of APIs for big data.
- Strong understanding of computer architecture and performance optimization.
- Strong communication and collaboration skills. 

If you are passionate about developing and deploying machine learning algorithms at scale, and want to join a dynamic team working on cutting-edge technology, we encourage you to apply for this position.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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